Lecture 2

class: center, middle, inverse, title-slide

# Lecture 2
## New variales and Plots
### Psych 10 C
### University of California, Irvine
### 05/28/2022

---

## Load data into R

- We will keep working with the memory data from last class

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---

## Creating new variables

- Sometimes we will like to work with some transformation of the variables
that we have on a data file.

--

- For example we could want to have a variable that tells us if the number 
of correctly recalled words was from the first or second test.

--

This can make some plots easier to make!

---

## Creating a new variable

- We will create a new variable that takes the value "test-1" if the test was 
perforned after 300 seconds of study and takes the value "test-2" if it was 
3600 seconds after:

```r
memory <- memory %>% 
  mutate("test_id" = ifelse(test = time_test == 300, 
                            yes = "test_1", no = "test_2"))
head(x = memory, n = 4)
```

```
# A tibble: 4 × 5
     id   age correct time_test test_id
  <dbl> <dbl>   <dbl>     <dbl> <chr>  
1     1    20      46       300 test_1 
2     2    29      49       300 test_1 
3     3    29      48       300 test_1 
4     4    25      44       300 test_1 
```

---

## Creating a new variable

- Using the **`mutate`** function we can create new variables using other functions 
in R (**`ifelse`** is a function in R)

--

- We will use our new variable to create plots of the variables that we are 
interested in.

---

class: inverse, center, middle

# Plots

## Histograms

---

## Histograms

- One of the ways in which we can visualize data is using histograms.

--

- A histogram represents a count of the number of times that a variable has 
appeared in our data.

---

count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
*ggplot(data = memory)
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_01_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
* aes(x = correct)
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_02_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
  aes(x = correct) +
* aes(fill = test_id, color = test_id)
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_03_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
  aes(x = correct) +
  aes(fill = test_id, color = test_id) +
* geom_histogram(position="identity",
*                binwidth = 1,
*                alpha = 0.4)
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_04_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
  aes(x = correct) +
  aes(fill = test_id, color = test_id) +
  geom_histogram(position="identity",
                 binwidth = 1,
                 alpha = 0.4) +
* theme_classic()
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_05_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
  aes(x = correct) +
  aes(fill = test_id, color = test_id) +
  geom_histogram(position="identity",
                 binwidth = 1,
                 alpha = 0.4) +
  theme_classic() +
* xlab("Number of correct recalls")
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_06_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
  aes(x = correct) +
  aes(fill = test_id, color = test_id) +
  geom_histogram(position="identity",
                 binwidth = 1,
                 alpha = 0.4) +
  theme_classic() +
  xlab("Number of correct recalls") +
* ylab("Frequency")
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_07_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
  aes(x = correct) +
  aes(fill = test_id, color = test_id) +
  geom_histogram(position="identity",
                 binwidth = 1,
                 alpha = 0.4) +
  theme_classic() +
  xlab("Number of correct recalls") +
  ylab("Frequency") +
* guides(fill = guide_legend("Test order"), color = "none")
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_08_output-1.png)
]

---
count: false
 
### Histogram of correct recalls
.panel1-hist-code-auto[

```r
ggplot(data = memory) +
  aes(x = correct) +
  aes(fill = test_id, color = test_id) +
  geom_histogram(position="identity",
                 binwidth = 1,
                 alpha = 0.4) +
  theme_classic() +
  xlab("Number of correct recalls") +
  ylab("Frequency") +
  guides(fill = guide_legend("Test order"), color = "none") +
* theme(axis.title.x = element_text(size = 20),
*       axis.title.y = element_text(size = 20))
```
]
 
.panel2-hist-code-auto[
![](data:image/png;base64,#lec-2_files/figure-html/hist-code_auto_09_output-1.png)
]

<style>
.panel1-hist-code-auto {
  color: black;
  width: 38.6060606060606%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 80%
}
.panel2-hist-code-auto {
  color: black;
  width: 59.3939393939394%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 80%
}
.panel3-hist-code-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 80%
}
</style>

---

## Histograms

- One of the main problems with histograms is that their shape depends on our
choice of the withd of the bars!

--

- A change on the shape can change our interpretation of the results so we need 
to be careful when making our choice.

--

- In general we can use histograms when we have a numeric variable that we want 
to visualize.